






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin



@require_jieba
class 				_A			(     UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							Dict				= CpmAntTokenizer
    lowerCamelCase	:							int				= False



    def _a							(				self      :     Dict      )      ->							List[str]:
           super().setUp()

           __UpperCAmelCase     				=[
               """<d>""",
               """</d>""",
               """<s>""",
               """</s>""",
               """</_>""",
               """<unk>""",
               """<pad>""",
               """</n>""",
               """我""",
               """是""",
               """C""",
               """P""",
               """M""",
               """A""",
               """n""",
               """t""",
           ]
           __UpperCAmelCase     				=os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]      )
           with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""      ) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]      )      )



    @tooslow
    def _a							(				self      :     Optional[Any]      )      ->							Optional[int]:
           __UpperCAmelCase     				=CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b"""      )
           __UpperCAmelCase     				="""今天天气真好！"""
           __UpperCAmelCase     				=["""今天""", """天气""", """真""", """好""", """！"""]
           __UpperCAmelCase     				=tokenizer.tokenize(__SCREAMING_SNAKE_CASE      )
           self.assertListEqual(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				="""今天天气真好！"""
           __UpperCAmelCase     				=[tokenizer.bos_token] + tokens

           __UpperCAmelCase     				=[6, 9802, 14962, 2082, 831, 244]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      ) ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=tokenizer.decode(__SCREAMING_SNAKE_CASE      )
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''image_processor''', '''tokenizer''']
 __A			       =					'''ViTImageProcessor'''
 __A			       =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]=None							, lowercase_				:   Dict=None							, **lowercase_				:   List[str])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."							, lowercase_							, )
     _UpperCamelCase           =    kwargs.pop("feature_extractor")

   _UpperCamelCase           =    image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`.")
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`.")

   super().__init__(lowercase_							, lowercase_)







 def __call__(		self				:   Any							, lowercase_				:   List[str]=None							, lowercase_				:   int=None							, lowercase_				:   Optional[Any]=None							, lowercase_				:   Dict=None							, **lowercase_				:   Dict)						->	int:


   """simple docstring"""







   if text is None and visual_prompt is None and images is None:
     raise ValueError("You have to specify either text, visual prompt or images.")

   if text is not None and visual_prompt is not None:
     raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")

   if text is not None:
     _UpperCamelCase           =    self.tokenizer(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if images is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None and images is not None:
     _UpperCamelCase           =    {
         "pixel_values": image_features.pixel_values,
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   elif text is not None and images is not None:
     _UpperCamelCase           =    image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   elif visual_prompt is not None:
     _UpperCamelCase           =    {
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   else:
     return BatchEncoding(data=dict(**lowercase_)							, tensor_type=lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, *lowercase_				:   List[Any]							, **lowercase_				:   List[Any])						->	int:


   """simple docstring"""







   return self.tokenizer.batch_decode(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, *lowercase_				:   Any							, **lowercase_				:   int)						->	Optional[int]:


   """simple docstring"""







   return self.tokenizer.decode(*lowercase_							, **lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	str:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase_							, )
   return self.image_processor_class







 @property
 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase_							, )
   return self.image_processor




